TGFβ signaling regulates epithelial–mesenchymal plasticity in ovarian cancer ascites-derived spheroids
Bibliographic record
Abstract
Epithelial-mesenchymal transition (EMT) serves as a key mechanism driving tumor cell migration, invasion, and metastasis in many carcinomas. Transforming growth factor-beta (TGFβ) signaling is implicated in several steps during cancer pathogenesis and acts as a classical inducer of EMT. Since epithelial ovarian cancer (EOC) cells have the potential to switch between epithelial and mesenchymal states during metastasis, we predicted that modulation of TGFβ signaling would significantly impact EMT and the malignant potential of EOC spheroid cells. Ovarian cancer patient ascites-derived cells naturally underwent an EMT response when aggregating into spheroids, and this was reversed upon spheroid re-attachment to a substratum. CDH1/E-cadherin expression was markedly reduced in spheroids compared with adherent cells, in concert with an up-regulation of several transcriptional repressors, i.e., SNAI1/Snail, TWIST1/2, and ZEB2. Treatment of EOC spheroids with the TGFβ type I receptor inhibitor, SB-431542, potently blocked the endogenous activation of EMT in spheroids. Furthermore, treatment of spheroids with SB-431542 upon re-attachment enhanced the epithelial phenotype of dispersing cells and significantly decreased cell motility and Transwell migration. Spheroid formation was significantly compromised by exposure to SB-431542 that correlated with a reduction in cell viability particularly in combination with carboplatin treatment. Thus, our findings are the first to demonstrate that intact TGFβ signaling is required to control EMT in EOC ascites-derived cell spheroids, and it promotes the malignant characteristics of these structures. As such, we show the therapeutic potential for targeted inhibition of this pathway in ovarian cancer patients with late-stage disease.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".